FitRPLS {CPEg}R Documentation

RPLS

Description

performs prediction using Fort and Lambert-Lacroix (2005) RPLS algorithm

Usage

FitRPLS(X, y, Xt, yt, LambdaRange = c(0.01, 1), ncompMax = 20)

Arguments

X design matrix, training sample
y binary response vector of 1 and -1
Xt design matrix, test sample
yt binary response vector of 1 and -1
LambdaRange tuning parameter
ncompMax number of components to use

Details

Value

list with two components: misclassification rates and confusion matricies for both the training and test samples.

Note

The columns of the data matrices X and Xt may not be standardized, since standardizing is performed by the function rpls as a preliminary step before the algorithm is run. The procedure described in Fort and Lambert-Lacroix (2005) is used to determine latent components.

Author(s)

MSI and AIM

References

G. Fort and S. Lambert-Lacroix (2005). Classification using Partial Least Squares with Penalized Logistic Regression, Bioinformatics, vol 21, n 8, 1104-1111.

http://cran.r-project.org/src/contrib/Descriptions/plsgenomics.html

http://www-lmc.imag.fr/lmc-sms/Sophie.Lambert

See Also

FitLDA

Examples

data(Golub)
attach(Golub)
#FitRPLS(X, y, Xt, yt)

[Package CPEg version 1.1 Index]